Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model
AbstractTime-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
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Bibliographic InfoPaper provided by Athens University of Economics and Business in its series DEOS Working Papers with number 1228.
Date of creation: 30 Jul 2012
Date of revision:
Dynamic heteroskedasticity; in mean models; time varying parameter; Markov chain Monte Carlo; simulated EM algorithm; Bayesian inference;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-11-03 (All new papers)
- NEP-ECM-2012-11-03 (Econometrics)
- NEP-ETS-2012-11-03 (Econometric Time Series)
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